TFPF

小波脊线提取crazy climber算法的文献出处(The wavelet ridge extract crazy climber algorithm literature source)

结合维纳滤波的小波降噪方法，滤波效果很好(Combination of Wiener filtering of wavelet noise reduction method, filter works well)

图像交互是分割 graph cut,实现交互式分割，并且可以连续分割(Interactive image is split graph cut, to achieve interactive segmentation, and can be continuously split)

使用matlab编写的啤酒瓶缺陷检测程序，测试通过（毕设）(Prepared using matlab beer bottle defect detection procedures, test (complete set))

说明：  SAR合成孔径雷达的点目标仿真报告，含matlab代码(SAR synthetic aperture radar point target simulation report, including Matlab code)

指纹识别 能够快速的识别出指纹库中的指纹，是否与输入的待检测的指纹相同，效果很好(Fingerprint identification can quickly identify the fingerprint of fingerprints, whether the same as the input fingerprint to be detected, the effect is very good)

在尽可能好的代表原始数据的前提下，PCA通过线性变换将高维空间中的样本数据投影到低维空间中。(In the best possible on behalf of the original data, under the precondition of PCA through linear transformation in the high-dimensional space sample data projection to low dimensional space)

说明：  获取图像灰度共生矩阵特征值代码，包括能量、熵、惯性矩和相关度的均值和标准差，已测试，代码及注释清晰。(The eigenvalue codes of gray level co-occurrence matrix, including the mean and standard deviation of energy, entropy, moment of inertia and correlation, have been tested and the codes and annotations are clear.)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)

从眼动仪提取数据，根据高斯核函数得到感兴趣区域(Extract data from the eye tracker, based on the Gaussian kernel function to get the area of ​ ​ interest)